AudioDatasetDir_Original = '/home/bxyan/Science/lungsound/data/ICBHI/icbhi_dataset/audio_text_data'
PatientFoldFile = '/home/bxyan/Science/lungsound/data/ICBHI/patient_list_foldwise.txt'

import os 

ProjectDir = os.path.dirname(os.path.abspath(__file__))



import torch
import os

BATCH_SIZE = 4 # increase / decrease according to GPU memeory
# RESIZE_TO = 512 # resize the image for training and transforms
NUM_EPOCHS = 100 # number of epochs to train for
DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# DEVICE = torch.device('cpu')
print(DEVICE)
# classes: 0 index is reserved for background
CLASSES = [
    'background', 'breath_cycle'
]
NUM_CLASSES = 2

# location to save model and plots
OUT_DIR = os.path.join(ProjectDir, 'temp')
SAVE_PLOTS_EPOCH = 2 # save loss plots after these many epochs
SAVE_MODEL_EPOCH = 2 # save model after these many epochs
